PSO.md

Particle Swarm Optimization

Overview

Particle Swarm Optimization, a population-based approach, generates several solutions in every evaluation and intended for simulating social behaviour, as a stylized representation of the movement of organisms in a bird flock or fish school. The algorithm was simplified and it was observed to be performing optimization.(cited from Wikipedia)

Pseduocode

#(I)Initialization
Initialize position s & velocities v of all particles
Initialize the personal best of particles pb and global best gb 



while not met termination condition:

    #(T)Transition
    v_{t+1} = new_velocity(s_{t},v_{t},pb_{t},gb_{t})
    s_{t+1} = new_position(s_{t}, v_{t+1})
    
    #(E)Evaluaiton
    pb = update_velocity(s_{t+1})
    gb = update_position(v_{t+1})
    Local_fitness = argmin(pb)

    #(D)Determination
    if Local_fitness < Global_fitness:
        Global_fitness = Local_fitness

return Global_fitness

Flowchart

Instructions for running on local machine

1. packages used in this projects:

   • numpy==1.24.2
   • matplotlib==3.7.1

2. Execution

   #python -m Algorithm.PSO [Dimension][a1][a2]
   python -m Algorithm.PSO 0.8 2.0 4 3

3. Folder organiation

   • Each algorithm will generate two files:
     • {filename}.png: show the trend/process of certain algo.
     • {filename}.csv: record every global optimal in every iterations
   • Check all the result in result folder

4. Discussion